A robust and secure video recovery scheme with deep compressive sensing

Abstract

In this paper, we propose a secure high quality video recovery scheme which can be useful for diverse applications like telemedicine and cloud-based surveillance. Our solution consists of deep learning-based video Compressive Sensing (CS) followed by a strategy for encrypting the compressed video. We split a video into a number of Groups Of Pictures (GOPs), where, each GOP consists of both keyframes and non-keyframes. The proposed video CS method uses a convolutional neural network (CNN) with a Structural Similarity Index Measure (SSIM) based loss function. Our recovery process has two stages. In the initial recovery stage, CNN is employed to make efficient use of spatial redundancy. In the deep recovery stage, non-keyframes are compensated by utilizing both keyframes and neighboring non-keyframes. Keyframes use multilevel feature compensation, and neighboring non-keyframes use single-level feature compensation. Additionally, we propose an unpredictable and complex chaotic map, with a broader chaotic range, termed as Sine Symbolic Chaotic Map (SSCM). For encrypting compressed features, we suggest a secure encryption scheme consisting of four operations: Forward Diffusion, Substitution, Backward Diffusion, and XORing with SSCM based chaotic sequence. Through extensive experimentation, we establish the efficacy of our combined solution over i) several state-of-the-art image and video CS methods, and ii) a number of video encryption techniques.